Repair Inference after Representation-Alignment Failure

When a representation-alignment correction underperforms a task reference, which tested repair recovers more, and can the repair family reach that reference? We study this post-failure repair inference through matched component replacements and exact optimization over positive-scale repair paths. In THINGS-EEG2 image retrieval from electroencephalography (EEG) signals, we attribute the change in the ridge–Procrustes accuracy gap between people included in and excluded from encoder training. Direction receives greater attribution at the cohort mean. Yet for excluded people, replacing ridge's radius (centered output length) with the Procrustes radius recovers 3.46 percentage points more accuracy on average than replacing direction. The advantage is positive in all ten. In OfficeHome image–text retrieval, we scale centered outputs of fixed maps before adding back the fixed mean. Even allowing a separate label-informed positive scale for every query, the exact optimum gains 27.62 percentage points over the fitted correction but remains 9.43 points below the unmodified reference. Scale choice alone therefore cannot restore reference performance on these paths. For a second EEG encoder, the exact label-informed per-query optimum exceeds its subject-centered reference by 7.96 points, establishing attainability. Matched replacements quantify the recovery forgone by following attribution to the weaker tested repair. Exact optima determine whether the declared scale family can recover the reference.